After implementing and deploying a redesigned business process, it may happen that the new process does not meet our expectations. For example, certain types of unforeseen exceptions may arise, the processing time of some tasks may be much higher than expected due to these exceptions, and queues may build up to the extent that process participants start taking shortcuts due to high pressure, while customers become unsatisfied due to long waiting times. A first step to address these issues is to understand what is actually happening during the execution of the process. This is the goal of the process monitoring phase of the BPM lifecycle. This chapter gives an overview of process monitoring techniques and tools. The chapter first focuses on performance dashboards, both for offline and online monitoring. Next, it dives into process mining techniques, including methods for automated process discovery, conformance checking, performance mining, and variants analysis.
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- 4.A. Adriansyah, Aligning observed and modeled behavior. PhD thesis, Eindhoven University of Technology (2014)Google Scholar
- 10.A. Augusto, R. Conforti, M. Dumas, M. La Rosa, Split miner: Discovering accurate and simple business process models from event logs, in Proceedings of the IEEE International Conference on Data Mining (ICDM) (IEEE Computer Society, 2017)Google Scholar
- 11.A. Augusto, R. Conforti, M. Dumas, M. La Rosa, G. Bruno, Automated discovery of structured process models: Discover structured vs. discover and structure, in Proc. of the 35th International Conference on Conceptual Modeling (ER), Cham, Switzerland, 2016 (Springer, Berlin, 2016)Google Scholar
- 12.A. Augusto, R. Conforti, M. Dumas, M. La Rosa, F.M. Maggi, A. Marrella, M. Mecella, A. Soo, Automated discovery of process models from event logs: Review and benchmark. CoRR, abs/1705.02288 (2017)Google Scholar
- 41.W. Eckerson, Performance Dashboards: Measuring, Monitoring, and Managing Your Business, 2nd edn. (Wiley, New York, 2010)Google Scholar
- 54.L. García-Bañuelos, N.R.T.P. van Beest, M. Dumas, M. La Rosa, Complete and interpretable conformance checking of business processes. IEEE Trans. Softw. Eng. (2017). https://doi-org.vu-nl.idm.oclc.org/10.1109/TSE.2017.2668418 Google Scholar
- 64.P. Harmon, Analyzing activities. BPTrends Newsl. 1(4) (2003). http://www.bptrends.com
- 70.IEEE TaskForce on Process Mining, Process Mining Manifesto. http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto. Accessed October 2017, 2011
- 88.M. Leyer, D. Heckl, J. Moormann, Process performance measurement. Handbook on Business Process Management, Volume 2 (2015), pp. 227–241Google Scholar
- 171.S. Suriadi, M.T. Wynn, C. Ouyang, A.H.M. ter Hofstede, N.J. van Dijk, Understanding process behaviours in a large insurance company in australia: A case study, in Proceedings of the 25th International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science, vol. 7908 (Springer, Berlin, 2013), pp. 449–464CrossRefGoogle Scholar
- 175.N.R.T.P. van Beest, M. Dumas, L. García-Bañuelos, M. La Rosa, Log delta analysis: Interpretable differencing of business process event logs, in Proceedings of the 13th International Conference on Business Process Management (BPM) (Springer, Berlin, 2015), pp. 386–405Google Scholar
- 180.M.L. van Eck, X. Lu, S.J.J. Leemans, W.M.P. van der Aalst, PM ˆ2 : A process mining project methodology, in Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE) (Springer, Berlin, 2015), pp. 297–313Google Scholar
- 192.A. Weijters, J. Ribeiro, Flexible Heuristics Miner (FHM), in Proceedings of the International Conference on Computational Intelligence and Data Mining (CIDM) (IEEE Computer Society, 2011)Google Scholar